bb_aggregate: Aggregate Single Cell Gene Expression

In blaserlab/blaseRtools: R Tools for Blaser Lab Data Analysis

Aggregate Single Cell Gene Expression

Description

Generates a matrix of counts aggregated by gene and/or cell group.

Usage

bb_aggregate(
  obj,
  assay = "RNA",
  experiment_type = "Gene Expression",
  gene_group_df = NULL,
  cell_group_df = NULL,
  norm_method = c("log", "binary", "size_only"),
  pseudocount = 1,
  scale_agg_values = TRUE,
  max_agg_value = 3,
  min_agg_value = -3,
  binary_min = 0,
  exclude.na = TRUE
)

Arguments

obj	A Seurat or cell data set object
assay	Gene expression assay to use for aggregation; currently only applies to Seurat objects, Default: 'RNA'
gene_group_df	A 2-column dataframe with gene names or ids and gene groupings, Default: NULL
cell_group_df	A 2-coumn dataframe with cell ids and gene groupings, Default: NULL
norm_method	Gene normalization method, Default: c("log", "binary", "size_only")
pseudocount	Pseudocount, Default: 1
scale_agg_values	Whether to scale the aggregated values, Default: TRUE
max_agg_value	If scaling, make this the maximum aggregated value, Default: 3
min_agg_value	If scaling, make this the minimum aggregated value, Default: -3
binary_min	Minimum value below which a cell is considered not to express a feature, Default: 0
exclude.na	Exclude NA?, Default: TRUE

Details

The best way to group genes or cells is by using bb_*meta and then select cell_id or feature_id plus one metadata column with your group labels.

Value

A dense or sparse matrix.

See Also

cli_div, cli_alert normalized_counts, my.aggregate.Matrix character(0)

blaserlab/blaseRtools documentation built on April 14, 2025, 6:04 p.m.